01 - we feel fine | jonathan harris + Sep Kamvar6334: Sentient Cities as Cybernetic, Real-time, Control Systems: Theory and Practice of Urban Sensing description: each particle represents a different feeling portrayed across the internet. Once clicked on, its data is conveyed 01 - we feel fine | jonathan harris + Sep Kamvar 6334: Sentient Cities as Cybernetic, Real-time, Control Systems: Theory and Practice of Urban Sensing description: feeling filter 01 - we feel fine | jonathan harris + Sep Kamvar 6334: Sentient Cities as Cybernetic, Real-time, Control Systems: Theory and Practice of Urban Sensing description: breakdown of feeling in chart format 01 - we feel fine | jonathan harris + Sep Kamvar 6334: Sentient Cities as Cybernetic, Real-time, Control Systems: Theory and Practice of Urban Sensing description: image and feeling coincided with one another 01 - we feel fine | jonathan harris + Sep Kamvar 6334: Sentient Cities as Cybernetic, Real-time, Control Systems: Theory and Practice of Urban Sensing description: image and feeling coincided with one another 01 - we feel fine | jonathan harris + Sep Kamvar 6334: Sentient Cities as Cybernetic, Real-time, Control Systems: Theory and Practice of Urban Sensing description: queries 1. How was the data collected? the data is collected by scraping the internet blogs, posts, newsfeeds, etc. 2. Why was the data collected? to trace to footprints left behind on the internet 3. What is interesting about the data? its representation as an interactive visual medium 4. What stories about urban dynamics can the collected data tell? shows the similiarities and variabilities that exist between people’s emotions through out the world 5. What sort of questions about urban dynamics can be answered by looking at the data? data about either a specific place, and the mood of a population can be perceived by the specific data examined. 6. Who is the target audience of the data visualization? the global community 7. What are the audience’s goals when approaching the visualization? to explore what the visualization is telling them, and to stumble upon various ways of interacting with the data. 8. What do they stand to learn? people in different locations may feel similarly to themselves, whilst people in the same place as them may share different opinions. 9. What sort of control are they offered over the interface that delivers the information? a great amount as multiple filters and ways to interact with the data can be utilized 10. How is the question of the magnitude of the data dealt with limiting the collected data, limiting the dimensionsons in the data set to be represented, or abstracting the data? the data deals primarily with how people feel. This word ‘feel’ needs to be in their comment to be matched as a hit for this site to post it. This enables, but limits the type of data that can be extracted to a more cohesive sentence rather than a single word responses. 11. How does the original question to be addressed operate as the bench mark for eliminating unnecessary details in the data visualization? It focuses the user, so as to not be overwhelmed by data. 12. How are the particular patterns highlighted through visualization techqniques in order of their importance? by codefying the information with colors, categories and abundance. 13. Is the data of static or dynamic in nature? dynamic 14. If the data is dynamic in nature, what happens when it starts to change? how does the audience interact with this ‘live’ data? How is the data that changes over time unraveled? either other particles enter the visualization, or particles begin to change out to more current feelings. It is not unraveled, but can be extracted upon by filters since it is a living ever growing site of data. 01 - we feel fine | jonathan harris + Sep Kamvar 6334: Sentient Cities as Cybernetic, Real-time, Control Systems: Theory and Practice of Urban Sensing